方法对比
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| 多模态问题解答× | 多模态Transformer× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015 | 2019–2021 |
| 提出者≠ | Antol, S. et al. (VQA team, Facebook AI Research / Virginia Tech) | Lu et al. (ViLBERT); Radford et al. (CLIP) |
| 类型≠ | Supervised multimodal learning | Cross-modal attention-based deep learning model |
| 开创性文献≠ | Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). VQA: Visual Question Answering. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2425–2433. DOI ↗ | Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗ |
| 别名 | Multimodal QA, Cross-modal question answering, Visual question answering, VQA | multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer |
| 相关 | 5 | 5 |
| 摘要≠ | Multimodal question answering (Multimodal QA) is a class of deep-learning methods that answer natural-language questions by jointly reasoning over information from multiple modalities — most commonly text and images, but also video, audio, and structured tables. Introduced prominently through the VQA benchmark in 2015, it has since expanded into a broad research area powering document understanding, medical diagnosis assistance, and embodied AI. | A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis. |
| ScholarGate数据集 ↗ |
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